Compound specific isotope analysis of individual amino acids in reef corals
CB Wall, RD Gates, BN Popp
9/25/2018
require("knitr")
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
###############
# Carbon and Nitrogen Isotopic Analysis of Individual Amino Acids in Montipora capitata
# Author: C. Wall
# Collaborators: Brian Popp, Ruth Gates
# Institution: University of Hawai'i at Mānoa
###############
# load packages
library(devtools)
install_github("ggbiplot", "vqv")
library(ggbiplot)
require(graphics)
library(dplyr)
library("effects")
library(plyr)
library(plotrix)
library("cowplot")Carbon
Carbon in amino acids of plankton, Symbiodinium and coral host tissues.
######## ########
## Carbon
######## ########
rm(list=ls())
d13C.dat<-read.csv("data/d13C.CSIA.wide.csv") # wide form carbon data
colnames(d13C.dat)
d13C.dat$Fraction<-factor(d13C.dat$Fraction, levels=c("host", "symb", "plank"))
d13C.dat<-d13C.dat[ , !(names(d13C.dat) %in% c("Norleucine", "Aminoadipic.Acid", "Methionine"))] #remove Norleucine, Methionine, Aminoadipic AcidPCA by treatment
Run a PCA looking at effects of treatment.
######## ######## ######## ########
######## ######## ######## ######## by treatment
# PCA dataframe
PCA.df<-d13C.dat[, c(7:8,11:23)]
PC<- prcomp(PCA.df[,c(-1:-2)], center = TRUE, scale= TRUE)
PC.summary<-summary(PC)
ev<-PC$sdev^2
newdat<-PC$x[,1:4] # 2 PCAs explain 76% of variance
#plot(PC, type="lines", main="PC.area eigenvalues")
## PC1 and PC2
PC.fig1 <- ggbiplot(PC, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df[,1], ellipse = TRUE,
circle = FALSE) +
scale_color_discrete(name = '') +
theme_bw() +
scale_x_continuous(breaks=pretty_breaks(n=5))+
coord_cartesian(xlim = c(-8, 8), ylim=c(-4, 4))+
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.7)
print(PC.fig1)
ggsave("figures/carbon/PCA_d13C.trt.pdf", height=5, width=8, encod="MacRoman")PCA by fraction
Run a new PCA looking at the effects of fraction (plankton, host, symbiont)
######## ######## ######## ########
######## ######## ######## ######## by fraction
PC.fig2 <- ggbiplot(PC, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df[,2], ellipse = TRUE,
circle = FALSE) +
scale_color_discrete(name = '') +
theme_bw() +
coord_cartesian(xlim = c(-8, 8), ylim=c(-4, 4)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.7)
print(PC.fig2)
ggsave("figures/carbon/PCA_d13C.frac.pdf", height=5, width=8, encod="MacRoman")Models
Run models looking for effects of Fraction or the Treatment-Interaction (feeding/light).
d13C.dat2<-d13C.dat[!(d13C.dat$Fraction=="plank"),] #remove plankton for now
for(i in c(11:23)){
Y=d13C.dat2[,i]
mod<-aov(Y~Fraction+Treat.Int, data=d13C.dat2)
print(anova(mod))
plot(allEffects(mod), ylab=colnames(d13C.dat2)[i], cex.axis=0.5)
}## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 7.9280 7.9280 4.8489 0.05881 .
## Treat.Int 2 2.3861 1.1931 0.7297 0.51157
## Residuals 8 13.0802 1.6350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 43.514 43.514 16.281 0.003762 **
## Treat.Int 2 38.910 19.455 7.279 0.015818 *
## Residuals 8 21.382 2.673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 2.197 2.1971 0.4464 0.5228
## Treat.Int 2 5.526 2.7628 0.5614 0.5914
## Residuals 8 39.372 4.9215
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.5098 0.5098 0.1910 0.6736
## Treat.Int 2 13.3334 6.6667 2.4977 0.1436
## Residuals 8 21.3529 2.6691
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 3.0549 3.0549 1.2730 0.2919
## Treat.Int 2 4.4772 2.2386 0.9328 0.4324
## Residuals 8 19.1986 2.3998
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.1806 0.18060 0.1457 0.7126
## Treat.Int 2 1.8191 0.90953 0.7340 0.5097
## Residuals 8 9.9129 1.23912
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 2.301 2.3006 0.5090 0.4958
## Treat.Int 2 0.536 0.2681 0.0593 0.9428
## Residuals 8 36.157 4.5196
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 10.0959 10.0959 5.0226 0.05533 .
## Treat.Int 2 1.9626 0.9813 0.4882 0.63089
## Residuals 8 16.0806 2.0101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.9366 1.93656 0.8704 0.3781
## Treat.Int 2 1.1095 0.55476 0.2494 0.7851
## Residuals 8 17.7984 2.22480
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 15.4184 15.4184 15.5000 0.004314 **
## Treat.Int 2 7.1774 3.5887 3.6077 0.076423 .
## Residuals 8 7.9579 0.9947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.4717 0.47171 0.3402 0.5758
## Treat.Int 2 1.7167 0.85836 0.6190 0.5624
## Residuals 8 11.0933 1.38666
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.4274 0.42736 0.2195 0.6519
## Treat.Int 2 3.7940 1.89702 0.9743 0.4181
## Residuals 8 15.5772 1.94716
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.7150 0.71500 0.4577 0.5178
## Treat.Int 2 0.0828 0.04142 0.0265 0.9739
## Residuals 8 12.4980 1.56225
# Almost Fraction effect for Alanine, Proline
# Fraction effect for: Glycine, Glutamic acid
# Treatment effect for: Glycine
# Almost Treatment effect: Glutamic Acid-New dataframe (long format) here to make figures. Same data as above.
###########
###########
# CSAA.dat long
d13C.dat.long<-read.csv("data/d13C.CSIA.long.csv")
#str(d13C.dat.long)
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Methionine"),]
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Norleucine"),]
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d13C.dat.long$AA.short<-mapvalues(d13C.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glu", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d13C.dat.long$AA.short<-factor(d13C.dat.long$AA.short, levels=c("Ala","Asp", "Glu", "Ile", "Leu", "Pro", "Val", "Gly", "Lys", "Ser", "Phe", "Thr", "Tyr"))
# looking at average trophic and sourceAA
# trophic example = glutamate, source = phenylalanine
# Source AA not enriched with trophic transfers
# Trophic AA change with trophic transfers... these are the ones below
d13C.dat.long$AA.cat<-ifelse(d13C.dat.long$AA.short=="Asp" | d13C.dat.long$AA.short=="Glu" |
d13C.dat.long$AA.short=="Ala" | d13C.dat.long$AA.short=="Ile" |
d13C.dat.long$AA.short=="Leu" | d13C.dat.long$AA.short=="Val" |
d13C.dat.long$AA.short=="Pro", "Troph", "Source")
mod<-lm(d13C.value~AA.short+Treat.Int*Fraction, data=d13C.dat.long)
plot(allEffects(mod))
dfC<-d13C.dat.long
dfC<-dfC[!(dfC$AA.short == "Thr"), ] # removing Thr because not source or trophicFigures
- all AA pooled across fractions, showing d13C.CSIA_Fraction
######## Figures
df.mean<-aggregate(d13C.value~AA.short+Fraction, data=dfC, mean, na.rm=TRUE)
df.n<-aggregate(d13C.value~AA.short+Fraction, data=dfC, length)
df.SD<-aggregate(d13C.value~AA.short+Fraction, data=dfC, sd, na.rm=TRUE)
colnames(df.SD)[3]="SD"
df.mean<-cbind(df.mean, df.SD[3])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
Fig.formatting<-(theme_classic()) +
theme(text=element_text(size=10),
axis.line=element_blank(),
legend.text.align = 0,
legend.text=element_text(size=10),
#legend.title = element_blank(),
panel.border = element_rect(fill=NA, colour = "black", size=1),
aspect.ratio=1,
axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=10),
axis.text.x=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=8)) +
theme(legend.key.size = unit(0.4, "cm")) +
theme(aspect.ratio=1) +
theme(panel.spacing=unit(c(0, 0, 0, 0), "cm"))
######
# all AA pooled by fraction
pd <- position_dodge(0.5) #offset for error bars
ggplot(df.mean, aes(x=AA.short, y=d13C.value)) +
geom_errorbar(aes(ymin=d13C.value-SD, ymax=d13C.value+SD, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=3, pch=19, position=pd, aes(color=Fraction)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4.0, y=0, color="gray40") +
annotate(geom="text", label="Source-AA", x=10.0, y=0, color="gray40") +
ggtitle("d13C by fraction, treatments pooled") +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
ylab(ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)")))) +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
Fig.formatting
ggsave("figures/carbon/d13C.CSIA_Fraction.pdf", height=5, width=8, encod="MacRoman")- d13C by fraction and treatments, showing d13C.CSIA_frac.trt
#################
#################
# d13C by fraction and treatments
df.mean<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, mean, na.rm=TRUE)
df.n<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, length)
df.SD<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, sd, na.rm=TRUE)
colnames(df.SD)[4]="SD"
df.mean<-cbind(df.mean, df.SD[4])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
pd <- position_dodge(0.0) #offset for error bars
ggplot(df.mean, aes(x=AA.short, y=d13C.value)) +
geom_point(size=3, position=pd, aes(shape=Treat.Int, color=Fraction, group=Treat.Int)) +
ggtitle("d13C by fraction and treatments") +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=0, color="gray40") +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
ylab(ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)")))) +
Fig.formatting
ggsave("figures/carbon/d13C.CSIA_frac.trt.pdf", height=5, width=8, encod="MacRoman")- all d13C amino acids: showing d13C.CSIA_Trt.alone
##################
# all d13C amino acids
df.mean2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, mean, na.rm=TRUE)
df.SD2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, na.rm=TRUE, sd)
df.SD2[is.na(df.SD2)] <- 0
colnames(df.SD2)[3]="SD"
df.mean2<-cbind(df.mean2, df.SD2[3])
pd <- position_dodge(0.5) #offset for error bars
ggplot(df.mean2, aes(x=AA.short, y=d13C.value, group=Treat.Int)) +
geom_errorbar(aes(ymin=d13C.value-SD, ymax=d13C.value+SD, color=Treat.Int), size=.5, width=0, position=pd) +
geom_point(aes(color=Treat.Int), size=3, position=pd) +
ggtitle("d13C by treatments") +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=0, color="gray40") +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
scale_color_manual(values=c("gray40", "darkgoldenrod1", "coral", "skyblue2")) +
ylab(ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)")))) +
Fig.formatting
ggsave("figures/carbon/d13C.CSIA_Trt.alone.pdf", height=5, width=8, encod="MacRoman")Trophic and source AA
##################
# all trophic and source AA
AA.means<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC, mean, na.rm=TRUE); AA.means
AA.sd<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC, sd, na.rm=TRUE)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
pd <- position_dodge(0.5) #offset for error bars
ggplot(AA.means, aes(x=Treat.Int, y=d13C.value, group=AA.cat, color=Fraction)) +
geom_errorbar(aes(ymin=d13C.value-SD, ymax=d13C.value+SD), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction, shape=AA.cat), size=3, position=pd) +
ggtitle("d13C by fraction and Troph/Source") +
coord_cartesian(ylim=c(-30, -5)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed", "Plankton")) +
scale_color_manual(values=c("coral", "skyblue3", "springgreen3")) +
ylab(ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)")))) +
Fig.formatting
ggsave("figures/carbon/d13C.CSIA_EAA.pdf", height=5, width=8, encod="MacRoman")- source AA glycine, serine, and threonine removed, showing 13C.CSIA.no.glyserthr
##################
# source AA glycine and serine removed also threonine
dfC.trim<-d13C.dat.long[!(d13C.dat.long$AA.short=="Ser" |
d13C.dat.long$AA.short=="Gly" |
d13C.dat.long$AA.short=="Thr"),]
AA.means<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC.trim, mean, na.rm=TRUE)
AA.sd<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC.trim, na.rm=TRUE, sd)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
pd <- position_dodge(0.5) #offset for error bars
ggplot(AA.means, aes(x=Treat.Int, y=d13C.value, group=AA.cat, color=Fraction)) +
geom_errorbar(aes(ymin=d13C.value-SD, ymax=d13C.value+SD), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction, shape=AA.cat), size=3, position=pd) +
coord_cartesian(ylim=c(-30, -5)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed", "Plankton")) +
ggtitle("d13C no ser/gly/thr") +
scale_color_manual(values=c("coral", "skyblue3", "springgreen3")) +
ylab(ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)")))) +
Fig.formatting
ggsave("figures/carbon/d13C.CSIA.no.glyserthr.pdf", height=5, width=8, encod="MacRoman")- threonine in host and symbiont (microbial source), showing d13C.CSIA_EAA.thr
##################
# threonine in host and symbiont (microbial source)
dfC.thr<-d13C.dat.long[(d13C.dat.long$AA.short=="Thr"),]
AA.means<-aggregate(d13C.value~AA.short+Treat.Int+Fraction, data=dfC.thr, mean, na.rm=TRUE); AA.means## AA.short Treat.Int Fraction d13C.value
## 1 Thr D-F host -9.828662
## 2 Thr L-F host -12.037879
## 3 Thr L-NF host -9.718434
## 4 Thr Plank plank -17.895960
## 5 Thr D-F symb -10.332904
## 6 Thr L-F symb -9.844444
## 7 Thr L-NF symb -8.840278AA.sd<-aggregate(d13C.value~AA.short+Treat.Int+Fraction, data=dfC.trim, na.rm=TRUE, sd)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
ggplot(AA.means, aes(x=Treat.Int, y=d13C.value, color=Fraction)) +
geom_errorbar(aes(ymin=d13C.value-SD, ymax=d13C.value+SD), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction), size=3, position=pd) +
coord_cartesian(ylim=c(0, -30)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed", "Plankton")) +
ggtitle("d13C threonine") +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
ylab(ylab(expression(paste(delta^{13}, C[AA], " (\u2030, v-PDB)")))) +
Fig.formatting
ggsave("figures/carbon/d13C.CSIA_EAA.thr.pdf", height=5, width=8, encod="MacRoman")Nitrogen
######## ########
## Nitrogen
######## ########
d15N.dat<-read.csv("data/d15N.CSIA.wide.csv") # wide form carbon data
d15N.dat<-d15N.dat[ , !(names(d15N.dat) %in% c("Norleucine", "Aminoadipic.Acid", "Methionine"))]
d15N.dat$Fraction<-factor(d15N.dat$Fraction, levels=c("host", "symb", "plank"))PCA by treatment
# PCA dataframe
PCA.df<-d15N.dat[, c(7:8,11:23)]
PC<- prcomp(PCA.df[, c(-1:-2)], center = TRUE, scale= TRUE)
PC.summary<-summary(PC)
ev<-PC$sdev^2
newdat<-PC$x[,1:4] # 2 PCAs explain 74% of variance
#plot(PC, type="lines", main="PC.area eigenvalues")
######################## treatments
## PC1 and PC2
PC.fig3 <- ggbiplot(PC, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df[,1], ellipse = TRUE,
circle = FALSE) +
scale_color_discrete(name = '') +
theme_bw() +
coord_cartesian(xlim = c(-8, 5), ylim=c(-6, 6)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.7)
print(PC.fig3)
ggsave("figures/nitrogen/PCA_d15N.trt.pdf", height=5, width=8, encod="MacRoman")PCA by fraction
######################### fractions
## PC1 and PC2
PC.fig4 <- ggbiplot(PC, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df[,2], ellipse = TRUE,
circle = FALSE) +
scale_color_discrete(name = '') +
theme_bw() +
coord_cartesian(xlim = c(-8, 5), ylim=c(-6, 6)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.7)
print(PC.fig4)
ggsave("figures/nitrogen/PCA_d15N.frac.pdf", height=5, width=8, encod="MacRoman")- arrange the 4 plots:
#### compile the 4 PCA ###
library("cowplot")
plot_grid(PC.fig1, PC.fig3, PC.fig2, PC.fig4, ncol = 2)
ggsave("figures/PCAs.pdf", height=8, width=11, encod="MacRoman")
######models
Overall we see:
- Fraction effect for: Leucine, Proline, Aspartic Acid, Glutamic Acid, Tyrosine.
- Treatment effect for: Leucine
d15N.dat2<-d15N.dat[!(d15N.dat$Fraction=="plank"),] #remove plankton for now
for(i in c(11:23)){
Y=d15N.dat2[,i]
mod<-aov(Y~Fraction+Treat.Int, data=d15N.dat2)
print(anova(mod), cex=0.5)
plot(allEffects(mod), ylab=colnames(d15N.dat2)[i])
}## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 8.1676 8.1676 3.3922 0.1028
## Treat.Int 2 1.8579 0.9290 0.3858 0.6919
## Residuals 8 19.2618 2.4077
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 4.2262 4.2262 1.6503 0.2349
## Treat.Int 2 1.5693 0.7846 0.3064 0.7444
## Residuals 8 20.4868 2.5609
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 13.327 13.3269 2.6576 0.1417
## Treat.Int 2 3.612 1.8061 0.3602 0.7083
## Residuals 8 40.117 5.0147
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.0006 0.00062 0.0003 0.9860
## Treat.Int 2 1.6612 0.83058 0.4397 0.6589
## Residuals 8 15.1127 1.88908
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.2248 0.22482 0.2201 0.6515
## Treat.Int 2 0.0415 0.02076 0.0203 0.9799
## Residuals 8 8.1699 1.02123
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 9.9952 9.9952 19.9043 0.002107 **
## Treat.Int 2 3.8088 1.9044 3.7924 0.069430 .
## Residuals 8 4.0173 0.5022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.3383 0.3383 0.0908 0.7708
## Treat.Int 2 3.8576 1.9288 0.5178 0.6145
## Residuals 8 29.8002 3.7250
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 22.9354 22.9354 28.9356 0.0006623 ***
## Treat.Int 2 4.1174 2.0587 2.5973 0.1351371
## Residuals 8 6.3411 0.7926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 3.3328 3.3328 6.3165 0.03619 *
## Treat.Int 2 1.4198 0.7099 1.3455 0.31354
## Residuals 8 4.2210 0.5276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.1145 1.11452 2.3608 0.1630
## Treat.Int 2 0.9348 0.46741 0.9901 0.4129
## Residuals 8 3.7768 0.47210
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.4002 1.40019 0.5355 0.4852
## Treat.Int 2 0.8007 0.40036 0.1531 0.8605
## Residuals 8 20.9164 2.61455
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 16.163 16.163 8.9499 0.01729 *
## Treat.Int 2 0.128 0.064 0.0354 0.96532
## Residuals 8 14.448 1.806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.1051 0.10513 0.0799 0.7846
## Treat.Int 2 0.2452 0.12259 0.0931 0.9120
## Residuals 8 10.5298 1.31623
# Fraction effect for: Leucine, Proline, Aspartic Acid, Glutamic Acid, Tyrosine
# Treatment effect for: LeucineTrophic position
Trophic position using trophic AA gultamic acid (Glu) and source amino acid phenylalanine (Phe), following Chikarishi et al. 2009
- Trophic Position, showing TP.glu.phe
###
# scatter of glutamic.acid vs. phenylalanine
### ### ###
### ### ### trophic position using trophic (Glu) and source (Phe) AA, Chikarishi et al. 2009
d15N.dat # dataframe here## Species Genotype Tag Tank Light.Trt Feed.Trt Treat.Int Fraction
## 1 plankton plank NA plank plank plank plank plank
## 2 M capitata A 89 tank5 Light NoFeed L-NF host
## 3 M capitata A 89 tank5 Light NoFeed L-NF symb
## 4 M capitata B 90 tank5 Light NoFeed L-NF host
## 5 M capitata B 90 tank5 Light NoFeed L-NF symb
## 6 M capitata A 91 tank6 Light Fed L-F host
## 7 M capitata A 91 tank6 Light Fed L-F symb
## 8 M capitata B 92 tank6 Light Fed L-F host
## 9 M capitata B 92 tank6 Light Fed L-F symb
## 10 M capitata A 93 tank8 Dark Fed D-F host
## 11 M capitata A 93 tank8 Dark Fed D-F symb
## 12 M capitata B 94 tank8 Dark Fed D-F host
## 13 M capitata B 94 tank8 Dark Fed D-F symb
## Sample.ID Analysis.ID Alanine Glycine Threonine Serine Valine
## 1 PLANK B1V5 12.527909 5.6008427 -1.98126227 6.050563 9.787651
## 2 T5A-HOST B1V7.redo 7.847327 2.9142793 -2.84154593 2.471675 4.713940
## 3 T5A-SYMB B1V4 4.161511 2.4165936 -0.88769904 2.673792 5.625749
## 4 T5B-HOST B1V1 8.445000 4.9093333 2.41500000 6.512000 7.545000
## 5 T5B-SYMB B1V6 3.413051 4.4181020 3.14079699 5.217705 6.315661
## 6 T6A-HOST B2V6 6.664737 2.5136251 -2.07588316 3.121292 6.944947
## 7 T6A-SYMB B1V2 4.758715 4.0494840 0.06969189 2.942487 5.572360
## 8 T6B-HOST B2V1 5.672675 2.1437369 -1.03612090 3.501343 5.474639
## 9 T6B-SYMB B2V4 6.373143 6.1538639 4.21524652 4.945442 6.749994
## 10 T8A-HOST B1V3 5.758753 0.7749311 -2.22203089 3.351731 6.410436
## 11 T8A-SYMB B2V3 5.360115 2.0968911 -1.54835714 2.579622 4.776017
## 12 T8B-HOST B2V2 7.724146 3.7909118 -0.62332275 3.423008 6.353737
## 13 T8B-SYMB B2V5.redo 8.146049 5.0333102 1.27248727 3.935703 6.760420
## Leucine Isoleucine Proline Aspartic.acid Glutamic.acid Phenylalanine
## 1 6.715856 7.246781 11.988448 8.936740 11.552222 0.5698750
## 2 3.738042 5.378339 7.172414 6.307249 5.953692 1.2066225
## 3 3.470356 7.967821 5.148293 5.360155 5.426751 2.3384388
## 4 5.638000 6.414333 8.628333 8.073333 7.287667 -0.0760000
## 5 2.821053 7.518209 2.949745 5.197029 5.927785 3.6630468
## 6 4.422418 6.219415 5.728406 5.733161 6.024922 2.3619627
## 7 1.757357 6.828875 4.039185 4.905036 5.337607 -1.3231998
## 8 3.341359 4.355346 5.909505 5.718192 5.829240 0.6865165
## 9 2.366618 4.339027 2.784974 5.379592 5.609523 3.6194835
## 10 5.830963 9.929012 6.644103 6.168844 6.529514 2.1608311
## 11 2.689574 4.910402 4.348096 4.914364 5.030590 1.2952007
## 12 4.913521 5.277349 6.779098 6.622148 6.644920 1.7441324
## 13 3.827548 3.994467 5.001674 6.542721 7.280612 2.5901570
## Tyrosine Lysine
## 1 4.6172731 3.838999
## 2 2.9448377 2.827533
## 3 3.2175743 2.002628
## 4 1.8043333 3.494667
## 5 5.6859317 3.202733
## 6 1.9984845 3.030835
## 7 2.1849553 1.719356
## 8 2.6914821 3.466719
## 9 5.9918839 3.349517
## 10 0.5778758 1.860629
## 11 4.5429056 1.957223
## 12 2.6329649 3.807909
## 13 4.9536936 5.133633# glu = trophic AA (changing with food, show enrichment realtive to source
# phe = source AA (showlittle change with increasing trophic position, reflect d15N baseline)
# beta = 3.4 (difference in d15N values among trophic and source AAs in primary producers, @ TP=1)
# TDFAA = trophic discrimination factor: mean 15N enrichment of >=1 trophic vs. source AA per trophic level
d15N.dat<-d15N.dat %>% mutate(TP = ((Glutamic.acid - Phenylalanine - 3.4)/7.6) +1)
df.mean<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, mean, na.rm=TRUE)
df.SD<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, sd, na.rm=TRUE)
colnames(df.SD)[3]="SD"
df.mean<-cbind(df.mean, df.SD[3])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
ggplot(df.mean, aes(x=Treat.Int, y=TP)) +
geom_errorbar(aes(ymin=TP-SD, ymax=TP+SD, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=3, pch=19, position=pd, aes(color=Fraction)) +
ggtitle("Chikarishi trophic position") +
coord_cartesian(ylim=c(2.5, 0.5)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed", "Plankton")) +
ylab(ylab(expression(paste(delta^{15}, N[Glu-Phe], " (\u2030, AIR)")))) +
scale_color_manual(values=c("coral", "skyblue3", "springgreen3")) +
Fig.formatting
ggsave("figures/nitrogen/TP.glu.phe.pdf", height=5, width=8, encod="MacRoman")###########
###########
# d15N.CSIA.dat long
d15N.dat.long<-read.csv("data/d15N.CSIA.long.csv")
#str(d15N.dat.long)
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Methionine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Norleucine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d15N.dat.long$AA.short<-mapvalues(d15N.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glu", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d15N.dat.long$AA.short<-factor(d15N.dat.long$AA.short, levels=c("Ala","Asp", "Glu", "Ile", "Leu", "Pro", "Val", "Gly", "Lys", "Ser", "Phe", "Thr", "Tyr"))
# looking at average Trophic and Source
d15N.dat.long$AA.cat<-ifelse(d15N.dat.long$AA.short=="Asp" | d15N.dat.long$AA.short=="Glu" |
d15N.dat.long$AA.short=="Ala" | d15N.dat.long$AA.short=="Ile" |
d15N.dat.long$AA.short=="Leu" | d15N.dat.long$AA.short=="Val" |
d15N.dat.long$AA.short=="Pro", "Troph", "Source")
dfN<-d15N.dat.long
mod<-lm(d15N.value~AA.short+Treat.Int*Fraction, data=d15N.dat.long)
plot(allEffects(mod))
dfN<-d15N.dat.long[!(d15N.dat.long$AA.short=="Thr"), ] # not good source, remove here- all AA pooled by fraction, showing d15N.CSIA_Fraction
######## Figures
pd <- position_dodge(0.5) #offset for error bars
df.mean<-aggregate(d15N.value~AA.short+Fraction, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+Fraction, data=dfN, length)
df.SD<-aggregate(d15N.value~AA.short+Fraction, data=dfN, sd, na.rm=TRUE)
df.SD[is.na(df.SD)] <- 0
colnames(df.SD)[3]="SD"
df.mean<-cbind(df.mean, df.SD[3])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
Fig.formatting<-(theme_classic()) +
theme(text=element_text(size=10),
axis.line=element_blank(),
legend.text.align = 0,
legend.text=element_text(size=10),
#legend.title = element_blank(),
panel.border = element_rect(fill=NA, colour = "black", size=1),
aspect.ratio=1,
axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=10),
axis.text.x=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=8)) +
theme(legend.key.size = unit(0.4, "cm")) +
theme(aspect.ratio=1) +
theme(panel.spacing=unit(c(0, 0, 0, 0), "cm"))
######
# all AA pooled by fraction
ggplot(df.mean, aes(x=AA.short, y=d15N.value)) +
geom_errorbar(aes(ymin=d15N.value-SD, ymax=d15N.value+SD, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=3, pch=19, position=pd, aes(color=Fraction)) +
ggtitle("d15N by fraction, treatments pooled") +
coord_cartesian(ylim=c(15, -5)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
ylab(ylab(expression(paste(delta^{15}, N[AA], " (\u2030, AIR)")))) +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
Fig.formatting
ggsave("figures/nitrogen/d15N.CSIA_Fraction.pdf", height=5, width=8, encod="MacRoman")- Fraction and treatment, showing d15N.CSIA_frac.trt
#################
df.mean<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, length)
df.SD<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, sd, na.rm=TRUE)
colnames(df.SD)[4]="SD"
df.mean<-cbind(df.mean, df.SD[4])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
ggplot(df.mean, aes(x=AA.short, y=d15N.value)) +
geom_point(size=3, aes(shape=Treat.Int, color=Fraction, group=Treat.Int)) +
ggtitle("d15N by fraction and treatments") +
coord_cartesian(ylim=c(15, -5)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
ylab(ylab(expression(paste(delta^{15}, N[AA], " (\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/d15N.CSIA_frac.trt.pdf", height=5, width=8, encod="MacRoman")- all amino acids, showing d15N.CSIA_Trt.alone.pdf
##################
# all d15N amino acids
df.mean2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.SD2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, na.rm=TRUE, sd)
df.SD2[is.na(df.SD2)] <- 0
colnames(df.SD2)[3]="SD"
df.mean2<-cbind(df.mean2, df.SD2[3])
ggplot(df.mean2, aes(x=AA.short, y=d15N.value, group=Treat.Int)) +
geom_errorbar(aes(ymin=d15N.value-SD, ymax=d15N.value+SD, color=Treat.Int), size=.5, width=0, position=pd) +
geom_point(aes(color=Treat.Int), size=3, position=pd) +
ggtitle("d15N by treatments") +
coord_cartesian(ylim=c(15, -5)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
scale_color_manual(values=c("gray40", "darkgoldenrod1", "coral", "skyblue2")) +
ylab(ylab(expression(paste(delta^{15}, N[AA], " (\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/d15N.CSIA_Trt.alone.pdf", height=5, width=8, encod="MacRoman")- all source and trophic AA, showing d15N.CSIA_EAA
##################
# all source and trophic) AA
AA.means<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN, mean, na.rm=TRUE); AA.means
AA.sd<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN, sd, na.rm=TRUE)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
ggplot(AA.means, aes(x=Treat.Int, y=d15N.value, group=AA.cat, color=Fraction)) +
geom_errorbar(aes(ymin=d15N.value-SD, ymax=d15N.value+SD), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction, shape=AA.cat), size=3, position=pd) +
ggtitle("d15N by fraction and Troph/EAA") +
coord_cartesian(ylim=c(12, 0)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
ylab(ylab(expression(paste(delta^{15}, N[AA], " (\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/d15N.CSIA_EAA.pdf", height=5, width=8, encod="MacRoman")- source and trophic AA w/o glycine, serine and threonine, showing d15N.CSIA_EAA.no.glyserthr
##################
# source and trophic AA w/o glycine serine and threonine
dfN.trim<-d15N.dat.long[!(d15N.dat.long$AA.short=="Ser" |
d15N.dat.long$AA.short=="Gly" | d15N.dat.long$AA.short=="Thr"), ]
AA.means<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN.trim, mean, na.rm=TRUE)
AA.sd<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN.trim, na.rm=TRUE, sd)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
ggplot(AA.means, aes(x=Treat.Int, y=d15N.value, group=AA.cat, color=Fraction)) +
geom_errorbar(aes(ymin=d15N.value-SD, ymax=d15N.value+SD), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction, shape=AA.cat), size=3, position=pd) +
coord_cartesian(ylim=c(14, 0)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
ggtitle("d15N no ser/gly/thr") +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
ylab(ylab(expression(paste(delta^{15}, N[AA], " (\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/d15N.CSIA_EAA.no.glyserthr.pdf", height=5, width=8, encod="MacRoman")- threonine in host and symbiont (microbial source), showing d15N.CSIA_EAA.thr
##################
# threonine in host and symbiont (microbial source)
dfN.thr<-d15N.dat.long[(d15N.dat.long$AA.short=="Thr"),]
AA.means<-aggregate(d15N.value~AA.cat+AA.short+Treat.Int+Fraction, data=dfN.thr, mean, na.rm=TRUE); AA.means
AA.sd<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN.trim, na.rm=TRUE, sd)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
ggplot(AA.means, aes(x=Treat.Int, y=d15N.value, color=Fraction)) +
geom_errorbar(aes(ymin=d15N.value-SD, ymax=d15N.value+SD), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction), size=3, position=pd) +
coord_cartesian(ylim=c(6, -6)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
ggtitle("d15N threonine") +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
ylab(ylab(expression(paste(delta^{15}, N[AA], " (\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/d15N.CSIA_EAA.thr.pdf", height=5, width=8, encod="MacRoman")Weighted means
These are the weighted means for trophic and source AA following Bradley et al. 2015.
- weighted means, showing wt.mean.d15N.CSIA
###########################
##################
#########
df.mean<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.SD<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, sd, na.rm=TRUE)
colnames(df.SD)[5]="SD"
df.mean<-cbind(df.mean, df.SD[5])
df.mean$mean.sd<-(df.mean$d15N.value/df.mean$SD)
df.mean$inv.sd<-(1/df.mean$SD)
# write.csv(df.mean, "wtmeans.csv") # use this to calculate weighted mean
# weighted mean is sum(mean.sd/inv.sd) for trophic AA, same for source AA
# delta.Tr.So (below) is difference in (weighted mean) Trophic AA - Source AA for host or symb, per treatment
#########
wt.mean<-read.csv("data/wt.means.d15N.csv")
wt.mean.df<-aggregate(wt.mean~AA.cat+Fraction+Treat.Int, data=wt.mean, mean, na.rm=TRUE)
pd <- position_dodge(0.0) #offset for error bars
ggplot(wt.mean.df, aes(x=Treat.Int, y=wt.mean)) +
geom_point(aes(color=Fraction, shape=AA.cat), size=3, position=pd) +
coord_cartesian(ylim=c(8, 0))+
ggtitle("weighted mean d15N") +
scale_color_manual(values=c("coral", "springgreen3")) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
ylab(ylab(expression(paste(delta^{15}, N[AA], " (\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/wt.mean.d15N.CSIA.pdf", height=5, width=8, encod="MacRoman")- delta trophic - source AA, showing wt.mean.d15N.TR.SO.CSIA
############
# delta trophic - source AA
wt.mean.df2<-aggregate(delt.Tr.So~AA.cat+Fraction+Treat.Int, data=wt.mean, mean, na.rm=TRUE)
ggplot(wt.mean.df2, aes(x=Treat.Int, y=delt.Tr.So)) +
geom_point(aes(color=Fraction), size=3) +
coord_cartesian(ylim=c(2, -5))+
ggtitle("weighted mean d15N") +
scale_color_manual(values=c("coral", "springgreen3")) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
ylab(ylab(expression(paste(delta^{15}, N[Trophic[AA]-Source [AA]], " (\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/wt.mean.d15N.TR.SO.CSIA.pdf", height=5, width=8, encod="MacRoman")Sum V
Calculate Sum-V, McCarthy et al. 2007. The sum-V parameter is a proxy for total heterotrophic resynthesis. It is defined as the average deviation in the d15N values of the trophic amino acids Ala, Asp, Glu, Ile, Leu, and Pro.
- showing d15N.sumV.CSIA
###########################
# dfN is dataframe
sV.df<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=d15N.dat.long, mean, na.rm=TRUE)
sV.df.sd<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=d15N.dat.long, sd, na.rm=TRUE)
colnames(sV.df.sd)[5]="SD"
sV.df$SD<-sV.df.sd$SD
# make dataframe for AA, that with su deviance = sum-V
sV.df2<-sV.df[c(sV.df$AA.short=="Ala" | sV.df$AA.short=="Glu" | sV.df$AA.short=="Asp" |
sV.df$AA.short=="Ile" | sV.df$AA.short=="Leu" | sV.df$AA.short=="Pro"),]
write.csv(sV.df2, "sumV.csv")
sumVdf<-read.csv("data/sumV.csv")
sumVdf<-sumVdf[!(sumVdf$Treat.Int=="Plank"), ]
ggplot(sumVdf, aes(x=Treat.Int, y=sumV)) +
geom_point(aes(color=Fraction), size=3) +
coord_cartesian(ylim=c(2, 7))+
ggtitle("sumV d15N") +
scale_color_manual(values=c("coral", "springgreen3", "")) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
ylab(ylab(expression(paste(delta^{15}, N[Sum-V], "(\u2030, AIR)")))) +
Fig.formatting
ggsave("figures/nitrogen/d15N.sumV.CSIA.pdf", height=5, width=8, encod="MacRoman")